A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

Sensors

Zhengyi Xu¹  Jianming Wei¹  Bo Zhang¹  Weijun Yang¹  Kourosh Khoshelham² 

[1] Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; E-Mail:;Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China; E-Mail

关键词: inertial sensors;    personal inertial navigation system;    zero velocity detector;    bayesian network;    kinesiology;   

DOI  :  10.3390/s150407708

来源: mdpi

【 摘 要 】

This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.